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Nanoparticle passivation by molecular recognition moieties (small molecules, aptamers, antibodies) imputes unique characteristics to nanoparticles (NPs) and unlocks their fascinating selectivity to targeting disease sites. Although non-passivated nanomaterials may archive selective targeting by exploring NP features and their interaction with biofluids proteins, such studies are scarce in the literature. Here, we report spherical manganese-based carbonaceous NPs (MnCQD), which specifically target mice kidneys after intravenous injection, even without direct surface chemical modification. Also, the NPs provide high image contrast in T1 magnetic resonance imaging (MRI) with subtle toxicological effects. The unexpected selectivity of MnCQD to the kidney has been examined based on their determined intrinsic properties and their interaction with two blood proteins: Bovine Serum Albumin (BSA), and Human Transferrin (HTF). More particularly, aspects such as size, composition, superficial charge, spectroscopic features, interaction mechanism, affinities, thermodynamic, and protein structural changes have been investigated. All these results highlight the excellent potential of MnCQD as a low toxic T1-MRI contrast agent and open the prospect of using non-functionalized NPs as a selective agent to target specific organs.

Analytics is a well-known form of capturing information about the user behavior of an application. Augmented reality applications deal with specific data such as the camera pose, not being supported by popular analytics frameworks. To fill such gap, this work proposes an analytics framework solution for augmented reality applications. It supports both markerbased and markerless augmented reality scenarios, collecting data related to camera pose and time spent by the user on each position. Besides the multiplatform capture tool, the framework provides a data analysis visualization tool capable of highlighting the most visited 3D positions, users main areas of interest over the marker plane, the 3D path performed by the camera and also a recovery of the content viewed by the user based on the collected camera pose information. Tests were performed using as case study a promotional campaign scenario and user behavior information was extracted using the proposed visualization tools.

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